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dc.contributor.authorVo, Ba Tuong
dc.contributor.authorTran, N.
dc.contributor.authorPhung, D.
dc.contributor.authorVo, Ba-Ngu
dc.identifier.citationVo, B.T. and Tran, N. and Phung, D. and Vo, B. 2017. Model-based classification and novelty detection for point pattern data, pp. 2622-2627.

© 2016 IEEE. Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance.

dc.titleModel-based classification and novelty detection for point pattern data
dc.typeConference Paper
dcterms.source.titleProceedings - International Conference on Pattern Recognition
dcterms.source.seriesProceedings - International Conference on Pattern Recognition
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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